Scenario Modeling and Edge-critical Scenario Generation for Autonomous Driving System

Extreme scenarios in autonomous driving, as well as unpredictable human behaviors, have become key factors limiting the development of autonomous driving systems (ADS). Therefore, effectively generating safety-critical scenarios is crucial for enhancing the safety of ADS. Existing methods for generating autonomous driving scenarios mainly rely on substantial road data collection and employ data-driven approaches combined with scenario generalization techniques. These methods are time-consuming, labor-intensive, and costly, making it difficult to effectively generate edge cases. In contrast, model-driven scenario modeling methods can construct logical scenario models to encapsulate complex scene features and effectively generate safety-critical scenarios. However, the challenge lies in designing a domain-knowledge-based visual scenario modeling language that supports the abstract modeling of complex driving scenarios and further explores edge-critical scenarios. To address these issues, this study proposes an approach for scenario modeling with SML4ADS2.0 and edge-critical scenario generation for autonomous driving systems. This method utilizes ontology-based modeling of scenarios within the ADS domain, combining formal quantitative evaluation with importance sampling to generate edge-critical scenarios. First, a model-driven scenario modeling method based on SML4ADS2.0 is proposed, using this language to construct models of autonomous driving scenarios. Second, the conversion of scenario models to stochastic hybrid automata (SHA) is implemented through model transformation rules and the model checking tool UPPAAL-SMC is used for quantitative evaluation and analysis of the scenario models. Subsequently, importance sampling techniques are employed to rapidly detect edge scenarios within the scenario space, effectively generating specific edge-critical scenarios from logical models. Finally, the method is demonstrated through case studies involving typical scenarios such as lane changes and overtaking. Experimental results indicate that this approach can effectively model scenarios and address the generation of safety-critical scenarios for ADS.